\begin{Schunk}
\begin{Sinput}
 myPois1<- glm(undercount ~ rural + perAA + equip, family = poisson, data = gavote)
 summary(myPois1) 
\end{Sinput}
\begin{Soutput}
Call:
glm(formula = undercount ~ rural + perAA + equip, family = poisson, 
    data = gavote)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-77.623  -11.794   -2.844    8.385  165.005  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  4.536222   0.010373  437.32   <2e-16 ***
ruralurban   1.216001   0.007586  160.30   <2e-16 ***
perAA        2.213451   0.020352  108.76   <2e-16 ***
equipOS-CC   0.754020   0.010677   70.62   <2e-16 ***
equipOS-PC   0.937110   0.011196   83.70   <2e-16 ***
equipPAPER  -1.504971   0.094463  -15.93   <2e-16 ***
equipPUNCH   1.556716   0.010217  152.37   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 184237  on 158  degrees of freedom
Residual deviance:  77702  on 152  degrees of freedom
AIC: 78893

Number of Fisher Scoring iterations: 6
\end{Soutput}
\begin{Sinput}
 predictOMatic(myPois1)
\end{Sinput}
\begin{Soutput}
$rural
  rural     perAA equip      fit
1 rural 0.2429811 LEVER 159.8201
2 urban 0.2429811 LEVER 539.1800

$perAA
  rural  perAA equip      fit
1 rural 0.0000 LEVER  93.3375
2 rural 0.1115 LEVER 119.4648
3 rural 0.2330 LEVER 156.3279
4 rural 0.3480 LEVER 201.6438
5 rural 0.7650 LEVER 507.5077

$equip
  rural     perAA equip       fit
1 rural 0.2429811 LEVER 159.82010
2 rural 0.2429811 OS-CC 339.70184
3 rural 0.2429811 OS-PC 407.95591
4 rural 0.2429811 PUNCH 758.06202
5 rural 0.2429811 PAPER  35.48384
\end{Soutput}
\end{Schunk}
